diff --git "a/DQN_v2.ipynb" "b/DQN_v2.ipynb" --- "a/DQN_v2.ipynb" +++ "b/DQN_v2.ipynb" @@ -15,21 +15,11 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 37, "metadata": { "id": "LNXxxKojNTiL" }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2022-12-22 23:23:58.395804: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA\n", - "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "import tensorflow as tf\n", "from tensorflow.keras import layers\n", @@ -45,6 +35,7 @@ "import random\n", "from matplotlib import pyplot as plt\n", "from sklearn.preprocessing import MinMaxScaler\n", + "import joblib\n", "\n", "import io\n", "import base64\n", @@ -53,7 +44,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 57, "metadata": {}, "outputs": [], "source": [ @@ -70,7 +61,7 @@ " self.epsilon_decay = 0.95 # Decay rate for epsilon\n", " self.update_rate = 5 # Number of steps until updating the target network\n", " self.batch_size = 100\n", - " self.learning_rate = 2.5e-4\n", + " self.learning_rate = 5e-4\n", " \n", " # Construct DQN models\n", " self.model = self._build_model()\n", @@ -100,9 +91,11 @@ " def _min_max(self):\n", " \"\"\"Run some steps to get data to do MINMAX scale \"\"\"\n", " state_arr = []\n", - " state = self.env.reset()\n", " state_arr.append(self.env.observation_space.high)\n", + " state_arr[0][1], state_arr[0][3] = 0,0\n", " state_arr.append(self.env.observation_space.low)\n", + " state_arr[1][1], state_arr[1][3] = 0,0\n", + " state = self.env.reset()\n", " for i in range(1000):\n", " random_action = self.env.action_space.sample()\n", " next_state, reward, done, info = self.env.step(random_action)\n", @@ -113,10 +106,12 @@ " state_arr = np.array(state_arr)\n", " self.scaler = MinMaxScaler()\n", " self.scaler.fit(state_arr)\n", + " # print(self.scaler.data_max_)\n", + " # print(self.scaler.data_min_)\n", "\n", " def _get_scaled_state(self, state):\n", - " # return state\n", - " return self.scaler.transform(state)\n", + " return state\n", + " # return self.scaler.transform(state.reshape(1,-1)).flatten()\n", "\n", " #\n", " # Trains the model using randomly selected experiences in the replay memory\n", @@ -130,10 +125,10 @@ " minibatch = random.sample(self.replay_buffer, self.batch_size)\n", " mb_arr = np.array(minibatch, dtype=object)\n", "\n", - " next_state_arr = self._get_scaled_state(np.stack(mb_arr[:,3]))\n", + " next_state_arr = np.stack(mb_arr[:,3])\n", " future_qvalues = self.target_model.predict(next_state_arr, verbose=0)\n", "\n", - " state_arr = self._get_scaled_state(np.stack(mb_arr[:,0]))\n", + " state_arr = np.stack(mb_arr[:,0])\n", " qvalues = self.model.predict(state_arr, verbose=0)\n", "\n", " for index, (state, action, reward, next_state, done) in enumerate(minibatch):\n", @@ -149,7 +144,7 @@ "\n", " # Perform gradient step\n", " X, y = np.array(X), np.array(y)\n", - " history = self.model.fit(self._get_scaled_state(X), y, batch_size = self.batch_size, shuffle = False, verbose=0)\n", + " history = self.model.fit(X, y, batch_size = self.batch_size, shuffle = False, verbose=0)\n", " # history = self.model.fit(X, y, epochs=1, verbose=0)\n", " # print(f\"Loss: {history.history['loss']} \")\n", "\n", @@ -164,6 +159,7 @@ " while current_step < total_steps:\n", " current_episode += 1\n", " state = self.env.reset()\n", + " state = self._get_scaled_state(state)\n", " total_reward = 0\n", " done = False\n", " while done != True:\n", @@ -172,13 +168,15 @@ " if np.random.random() > (1 - self.epsilon):\n", " action = random.randrange(self.action_size)\n", " else:\n", - " model_predict = self.model.predict(self._get_scaled_state(np.array([state])), verbose=0)\n", + " model_predict = self.model.predict(np.array([state]), verbose=0)\n", " action = np.argmax(model_predict)\n", "\n", " # step\n", " next_state, reward, done, info = self.env.step(action)\n", " total_reward += reward\n", "\n", + " next_state = self._get_scaled_state(next_state)\n", + "\n", " # add to buffer\n", " self.replay_buffer.append((state, action, reward, next_state, done))\n", "\n", @@ -189,47 +187,53 @@ " self.target_model.set_weights(self.model.get_weights())\n", " \n", " state = next_state\n", - " \n", + "\n", + " # update epsilon every 100 steps \n", + " if current_step % 20 == 0:\n", + " if self.epsilon > self.epsilon_min:\n", + " self.epsilon *= self.epsilon_decay\n", + "\n", " rewards.append(total_reward)\n", - " # update epsilon\n", - " if self.epsilon > self.epsilon_min:\n", - " self.epsilon *= self.epsilon_decay\n", + "\n", " #\n", " # Loads a saved model\n", " #\n", " def load(self, name):\n", " self.model.load_weights(name)\n", + " self.scaler = joblib.load(name+\".scaler\") \n", "\n", " #\n", " # Saves parameters of a trained model\n", " #\n", " def save(self, name):\n", " self.model.save_weights(name)\n", + " joblib.dump(self.scaler, name+\".scaler\") \n", "\n", " def play(self, state):\n", + " state = self._get_scaled_state(state)\n", " return np.argmax(self.model.predict(np.array([state]), verbose=0)[0])" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 58, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Model: \"sequential_4\"\n", + "Model: \"sequential_36\"\n", "_________________________________________________________________\n", " Layer (type) Output Shape Param # \n", "=================================================================\n", - " dense_14 (Dense) (None, 512) 2560 \n", + " dense_144 (Dense) (None, 512) 2560 \n", " \n", - " dense_15 (Dense) (None, 256) 131328 \n", + " dense_145 (Dense) (None, 256) 131328 \n", " \n", - " dense_16 (Dense) (None, 128) 32896 \n", + " dense_146 (Dense) (None, 128) 32896 \n", " \n", - " dense_17 (Dense) (None, 2) 258 \n", + " dense_147 (Dense) (None, 2) 258 \n", " \n", "=================================================================\n", "Total params: 167,042\n", @@ -238,1220 +242,1209 @@ "_________________________________________________________________\n", "epsilon:1.0 step:15 episode:1 last_score 0 \n", "epsilon:1.0 step:20 episode:1 last_score 0 \n", - "epsilon:1.0 step:25 episode:1 last_score 0 \n", - "epsilon:1.0 step:30 episode:1 last_score 0 \n", - "epsilon:1.0 step:35 episode:1 last_score 0 \n", - "epsilon:1.0 step:40 episode:1 last_score 0 \n", - "epsilon:1.0 step:45 episode:1 last_score 0 \n", - "epsilon:1.0 step:50 episode:1 last_score 0 \n", - "epsilon:0.95 step:55 episode:2 last_score 51.0 \n", - "epsilon:0.95 step:60 episode:2 last_score 51.0 \n", - "epsilon:0.9025 step:65 episode:3 last_score 9.0 \n", - "epsilon:0.9025 step:70 episode:3 last_score 9.0 \n", - "epsilon:0.9025 step:75 episode:3 last_score 9.0 \n", - "epsilon:0.9025 step:80 episode:3 last_score 9.0 \n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/viewitpro/projetos/anaconda3/envs/rl2/lib/python3.8/site-packages/sklearn/preprocessing/_data.py:474: RuntimeWarning: overflow encountered in subtract\n", - " data_range = data_max - data_min\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "epsilon:0.8573749999999999 step:85 episode:4 last_score 21.0 \n", - "epsilon:0.8573749999999999 step:90 episode:4 last_score 21.0 \n", - "epsilon:0.8573749999999999 step:95 episode:4 last_score 21.0 \n", - "epsilon:0.8573749999999999 step:100 episode:4 last_score 21.0 \n", - "epsilon:0.8145062499999999 step:105 episode:5 last_score 19.0 \n", - "epsilon:0.7737809374999999 step:110 episode:6 last_score 9.0 \n", - "epsilon:0.7737809374999999 step:115 episode:6 last_score 9.0 \n", - "epsilon:0.7737809374999999 step:120 episode:6 last_score 9.0 \n", - 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"epsilon:0.0009833015279105794 step:6035 episode:120 last_score 244.0 \n" ] } ], @@ -1465,7 +1458,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 59, "metadata": {}, "outputs": [], "source": [ @@ -1474,31 +1467,31 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 60, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Model: \"sequential_28\"\n", + "Model: \"sequential_38\"\n", "_________________________________________________________________\n", " Layer (type) Output Shape Param # \n", "=================================================================\n", - " dense_97 (Dense) (None, 512) 2560 \n", + " dense_152 (Dense) (None, 512) 2560 \n", " \n", - " dense_98 (Dense) (None, 256) 131328 \n", + " dense_153 (Dense) (None, 256) 131328 \n", " \n", - " dense_99 (Dense) (None, 128) 32896 \n", + " dense_154 (Dense) (None, 128) 32896 \n", " \n", - " dense_100 (Dense) (None, 2) 258 \n", + " dense_155 (Dense) (None, 2) 258 \n", " \n", "=================================================================\n", "Total params: 167,042\n", "Trainable params: 167,042\n", "Non-trainable params: 0\n", "_________________________________________________________________\n", - "Total reward 500.0\n" + "Total reward 439.0\n" ] } ], @@ -1515,11 +1508,68 @@ " total_reward +=reward\n", " state = observation\n", " if done: \n", - " print(f\"Total reward {total_reward}\")\n", " break\n", + "print(f\"Total reward {total_reward}\")\n", "eval_env.close()" ] }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 4.8000002e+00 0.0000000e+00 4.1887903e-01 0.0000000e+00]\n", + " [-4.8000002e+00 0.0000000e+00 -4.1887903e-01 0.0000000e+00]\n", + " [-4.5489166e-03 -2.4116948e-01 -4.1077949e-02 3.0230245e-01]\n", + " ...\n", + " [ 5.0607350e-02 1.7593256e-01 -8.8660561e-02 -4.2944443e-01]\n", + " [ 5.4126002e-02 3.7219086e-01 -9.7249448e-02 -7.4870765e-01]\n", + " [ 6.1569817e-02 5.6851023e-01 -1.1222360e-01 -1.0703416e+00]]\n", + "Max [4.8 2.2215695 0.41887903 2.1663814 ]\n", + "Min [-4.8 -1.4063605 -0.41887903 -2.5751066 ]\n", + "[ 0.0172858 0.00332159 -0.03439883 0.01944862]\n", + "[0.5018006 0.38856372 0.45893943 0.54720277]\n" + ] + } + ], + "source": [ + "\n", + "# def _min_max(env):\n", + "# \"\"\"Run some steps to get data to do MINMAX scale \"\"\"\n", + "# state_arr = []\n", + "# state_arr.append(env.observation_space.high)\n", + "# state_arr[0][1], state_arr[0][3] = 0,0\n", + "# state_arr.append(env.observation_space.low)\n", + "# state_arr[1][1], state_arr[1][3] = 0,0\n", + "# state = env.reset()\n", + "# for i in range(1000):\n", + "# random_action = env.action_space.sample()\n", + "# next_state, reward, done, info = env.step(random_action)\n", + "# state_arr.append(next_state)\n", + "# if done:\n", + "# state = env.reset()\n", + "\n", + "# state_arr = np.array(state_arr)\n", + "\n", + "# print(state_arr)\n", + "# scaler = MinMaxScaler()\n", + "# scaler.fit(state_arr)\n", + "# print(\"Max \",scaler.data_max_)\n", + "# print(\"Min \", scaler.data_min_)\n", + "# return scaler\n", + "\n", + "# env = gym.make('CartPole-v1')\n", + "# scaler = _min_max(env)\n", + "# state = env.reset()\n", + "# print(state)\n", + "# state = scaler.transform(state.reshape(1, -1)).flatten()\n", + "# print(state)\n" + ] + }, { "cell_type": "code", "execution_count": null,